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Challenge

Saudi $867m Battery Storage Health & Degradation Monitor

Operating $867m worth of battery energy storage facilities requires predicting Lithium-ion cell State-of-Health (SoH) degradation over thousands of charge-discharge cycles. Build an ML degradation monitoring model using Scikit-Learn and Python to forecast capacity loss based on operational temperature and C-rate stress. Maintain a Mean Absolute Error (MAE) under 1.5% SoH.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop a battery health (SoH) prediction model using Scikit-Learn and Python for Saudi BESS facilities.

Delivery guide

How work is evaluated

Evaluation

Evaluates battery State of Health (SoH) prediction accuracy against test cell degradation profiles.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

MAE Error Threshold Check

Ensures absolute prediction error is less than 1.5% SoH.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

Mean Absolute Error (SoH)

Percentage error in State-of-Health prediction. • target: 0.8 • range: 0-10

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Preprocess battery cell sensor telemetry (voltage, current, temperature, charge cycle count)

  • Train Random Forest / Gradient Boosting regression models on battery degradation curves

  • Predict remaining useful life (RUL) under desert thermal conditions

  • Output operational thermal cooling recommendations to slow degradation

Resources and assets

Reference links and supporting material

Dataset notes

Lithium-ion battery cell cycle degradation datasets recorded under high ambient temperature conditions.

Challenge setup

Evaluates battery State of Health (SoH) prediction accuracy against test cell degradation profiles.

Challenge input

JSON object with cycle_count, avg_temp_c, max_crate

Scikit-Learn

Standard Python ML toolkit for tabular predictive modeling.

Evaluated output

JSON object with predicted_soh_pct, mae_pct

Evaluation checks
  • Ensures absolute prediction error is less than 1.5% SoH.
  • Percentage error in State-of-Health prediction. • target: 0.8 • range: 0-10
Configured evaluation evidence
  • Benchmark: BatteryBench
  • Mean Absolute Error (SoH) target: 0.8
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Aim · Required
  • MLflow · Optional
  • Fixie · Optional

Evaluation contract

  • MAE Error Threshold Check · Weight 1
  • Mean Absolute Error (SoH) · Weight 1

Metrics

  • Mean Absolute Error (SoH) · Target 0.8

Benchmarks

  • BatteryBench
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with cycle_count, avg_temp_c, max_crate

    Output: JSON object with predicted_soh_pct, mae_pct

    Execution harness: Python

Evaluation tools

  • Scikit-Learn · Machine Learning Library

Recipe freeze marker

No recipe freeze time is recorded.

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